A systematic review and proposed framework for sustainable learning healthcare systems.

Background: The healthcare sector is a complex domain that faces challenges in effectively learning from practices and outcome data. The Learning Health System (LHS) has emerged as a potential framework to improve healthcare by promoting continuous learning. However, its adoption remains limited, often involving only a single clinical department or a part of the LHS cycle.

Reusable Generic Clinical Decision Support System Module for Immunization Recommendations in Resource-Constraint Settings.

Clinical decision support systems (CDSS) are routinely employed in clinical settings to improve quality of care, ensure patient safety, and deliver consistent medical care. However, rule-based CDSS, currently available, do not feature reusable rules. In this study, we present CDSS with reusable rules.

Towards Accountable E-Health Policies in the Nordic Countries.

The Nordic Countries are seen as forerunners in the field of digital health technologies and national implementation has been guided by sector specific strategies for many years. In the context of new European legislation such as the European Health Data Space (EHDS), a review of the existing strategies is indicated. The objective of this policy analysis is to assess and compare the scope, ambitions and extent of accountability in national-level digital health policies in the Nordic countries.

Nordic Citizens' Willingness to Share Digital Health Data.

The quality of the digital healthcare systems relies on citizens' willingness to share their digital health data. This makes citizens' use, perceptions, and attitudes towards digital healthcare systems pivotal. The study presented here examines Nordic citizens' willingness to share digital health data with healthcare providers and for research purposes.

Active Learning Pipeline to Identify Candidate Terms for a CDSS Ontology.

Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we explore an active learning approach to automatically identify candidate terms from publications, with manual verification later as a part of a deep learning model training and learning process.